





'''simple docstring'''






import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   [
    ("""bert.bert""", """visual_bert"""),
    ("""bert.cls""", """cls"""),
    ("""bert.classifier""", """cls"""),
    ("""token_type_embeddings_visual""", """visual_token_type_embeddings"""),
    ("""position_embeddings_visual""", """visual_position_embeddings"""),
    ("""projection""", """visual_projection"""),
]

lowerCAmelCase									=   [
    """nlvr2_coco_pre_trained.th""",
    """nlvr2_fine_tuned.th""",
    """nlvr2_pre_trained.th""",
    """vcr_coco_pre_train.th""",
    """vcr_fine_tune.th""",
    """vcr_pre_train.th""",
    """vqa_coco_pre_trained.th""",
    """vqa_fine_tuned.th""",
    """vqa_pre_trained.th""",
]






def   __A    (				a_    :	Union[str, Any]						):
		lowerCAmelCase  :     List[Any]       						= torch.load(a_   ,map_location="cpu"						)
		return sd






def   __A    (				a_    :	List[Any]   ,a_    :	Optional[int]   ,a_    :	str=rename_keys_prefix						):
		lowerCAmelCase  :     int       						= OrderedDict()
		lowerCAmelCase  :     Union[str, Any]       						= torch.arange(config.max_position_embeddings						).expand((1, -1)						)
		# detector_d = OrderedDict()
		for key in d:
				if "detector" in key:
						# detector_d[key.replace('detector.','')] = d[key]
						continue
				lowerCAmelCase  :     Optional[int]       						= key
				for name_pair in rename_keys_prefix:
						lowerCAmelCase  :     Dict       						= new_key.replace(name_pair[0]   ,name_pair[1]						)
				lowerCAmelCase  :     Tuple       						= d[key]
				if key == "bert.cls.predictions.decoder.weight":
						# Old bert code didn't have `decoder.bias`, but was added separately
						lowerCAmelCase  :     str       						= new_d["cls.predictions.bias"]
		return new_d






@torch.no_grad()
def   __A    (				a_    :	Any   ,a_    :	Union[str, Any]						):
		assert (
		    checkpoint_path.split("/"						)[-1] in ACCEPTABLE_CHECKPOINTS
		), f'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

		# Get Config
		if "pre" in checkpoint_path:
				lowerCAmelCase  :     Union[str, Any]       						= "pretraining"
				if "vcr" in checkpoint_path:
						lowerCAmelCase  :     Dict       						= {"visual_embedding_dim": 5_1_2}
				elif "vqa_advanced" in checkpoint_path:
						lowerCAmelCase  :     Tuple       						= {"visual_embedding_dim": 2_0_4_8}
				elif "vqa" in checkpoint_path:
						lowerCAmelCase  :     Any       						= {"visual_embedding_dim": 2_0_4_8}
				elif "nlvr" in checkpoint_path:
						lowerCAmelCase  :     Dict       						= {"visual_embedding_dim": 1_0_2_4}
				else:
						raise NotImplementedError(f'''No implementation found for `{checkpoint_path}`.'''						)
		else:
				if "vcr" in checkpoint_path:
						lowerCAmelCase  :     Optional[Any]       						= {"visual_embedding_dim": 5_1_2}
						lowerCAmelCase  :     List[Any]       						= "multichoice"
				elif "vqa_advanced" in checkpoint_path:
						lowerCAmelCase  :     List[Any]       						= {"visual_embedding_dim": 2_0_4_8}
						lowerCAmelCase  :     List[str]       						= "vqa_advanced"
				elif "vqa" in checkpoint_path:
						lowerCAmelCase  :     int       						= {"visual_embedding_dim": 2_0_4_8, "num_labels": 3_1_2_9}
						lowerCAmelCase  :     Union[str, Any]       						= "vqa"
				elif "nlvr" in checkpoint_path:
						lowerCAmelCase  :     List[str]       						= {
						    "visual_embedding_dim": 1_0_2_4,
						    "num_labels": 2,
						}
						lowerCAmelCase  :     Optional[int]       						= "nlvr"

		lowerCAmelCase  :     Dict       						= VisualBertConfig(**a_						)

		# Load State Dict
		lowerCAmelCase  :     List[str]       						= load_state_dict(a_						)

		lowerCAmelCase  :     Optional[int]       						= get_new_dict(a_   ,a_						)

		if model_type == "pretraining":
				lowerCAmelCase  :     Optional[Any]       						= VisualBertForPreTraining(a_						)
		elif model_type == "vqa":
				lowerCAmelCase  :     Union[str, Any]       						= VisualBertForQuestionAnswering(a_						)
		elif model_type == "nlvr":
				lowerCAmelCase  :     Optional[Any]       						= VisualBertForVisualReasoning(a_						)
		elif model_type == "multichoice":
				lowerCAmelCase  :     Tuple       						= VisualBertForMultipleChoice(a_						)

		model.load_state_dict(a_						)
		# Save Checkpoints
		Path(a_						).mkdir(exist_ok=a_						)
		model.save_pretrained(a_						)


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("""orig_checkpoint_path""", type=str, help="""A path to .th on local filesystem.""")
					parser.add_argument("""pytorch_dump_folder_path""", type=str, help="""Path to the output PyTorch model.""")
					lowerCAmelCase									=   parser.parse_args()
					convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase									=   {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



